Adaptive locality sensitive analysis representation learning via K-SVD algorithm
Kun Jiang, Zheng Liu, Lei Zhu · 2023
Recent years, analysis representation learning and its applications for classification have been well explored and applied, due to its flexible representation ability and low classification complexity. With a learned analysis dictionary, test samples can be transformed into a sparse subspace for classification efficiently. However, the underlying locality of sample data has rarely been explored and reliably married with analysis representation learning to enhance the discriminative capability of the classifier. In this paper, we propose a novel adaptive locality-sensitive analysis representation learning model for pattern classification (ALAR). It considers the intrinsic geometric properties by imposing adaptive weighted constrained graph regularization to uncover the geometric structure of the image data. Through the learned analysis dictionary, we transform the image to a new and compact space where the manifold assumption can be further guaranteed. Thus, the local geometrical structure of images can be preserved in sparse representation coefficients. Moreover, the ALAR model is iteratively solved by the synthesis K-SVD and gradient technique. Experimental results on image classification validate the performance superiority of our ALAR model.